NDLI: Phase specific optimal treatment for cancer using GA and swarm intelligence

Content Provider	IEEE Xplore Digital Library
Author	Algoul, S. Hossain, M.A. Alam, M.S. Majumder, M.A.
Copyright Year	2011
Description	Author affiliation: Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka, Bangladesh (Alam, M.S.) \|\| Department of Clinical Sciences, School of Life Sciences, University of Bradford, UK (Majumder, M.A.) \|\| Department of Computing, School of Computing, Informatics and Media, University of Bradford, UK (Algoul, S.) \|\| Department of Computer Science, School of Computing, Engineering and Information, University of Northumbria, UK (Hossain, M.A.)
Abstract	This paper presents an investigation into the development of a multi-objective optimal chemotherapy control model to reduce the number of cancer cells after a number of fixed treatment cycles with minimum side effects. A phase specific drug scheduling method using a close-loop control method with multi-objective techniques is proposed in this paper. Genetic Algorithm (GA) and particle swarm optimisation algorithm (PSO) are used to optimise the control solution for trading-off between the cell killing and toxic side effects. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused into the patient's body and multi-objective GA (MOGA) and multi-objective PSO (MOPSO) are used to find suitable parameters of the controller. The proposed algorithm is implemented, tested and verified through a set of experiments. Performances of the proposed methods demonstrated that both the MOGA and MOPSO approach can offer very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals. It is also noted that the MOGA based method offers better performance as compared to MOPSO and can reduce the number of proliferating and quiescent cells up to 72.2% and 60.4% respectively. Future research needs to evaluate the proposed scheduling with clinical data and experiments.
Starting Page	617
Ending Page	622
File Size	1087540
Page Count	6
File Format	PDF
ISBN	9781612849072
e-ISBN	9781612849089
DOI	10.1109/ICCITechn.2011.6164862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-22
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Cancer chemotherapy Genetic Algorithm Phase specific scheduling Feedback control Particle Swarm Algorithm Multi-objective optimisation
Content Type	Text
Resource Type	Article

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